In order to stabilise training for our generative network, we used the Spectral-Normalization GAN [1]} as our model backbone. We used conditional batch normalization [2]} in the object predictors to better normalize the object feature maps. The model has been adapted for 64x64 and 128x128 image sizes. The values of the six hyperparameters \(\lambda _{1}\)  to \(\lambda _{6}\)  are set to 0.01, 1, 8, 1,1 and 1, respectively. These values have been set experimentally. The Adam optimizer [3]} was to train all the models with batch size of 16 and 300,000 iterations in total. For more finer details, we will make our code publicly available.
